Intruder data classification using GM-SOM

Authors:
Petr Gajdoš;Pavel Moravec
Affiliations:
Department of Computer Science, FEECS, VŠB - Technical University of Ostrava, Ostrava-Poruba, Czech Republic;Department of Computer Science, FEECS, VŠB - Technical University of Ostrava, Ostrava-Poruba, Czech Republic
Venue:
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
Year:
2012

Citing 3
Cited 0

A parallel Kohonen algorithm for the classification of large spatial datasets

Computers & Geosciences - Special issue: neural network applications in the geosciences
Self-organizing maps

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A parallel growing architecture for self-organizing maps with unsupervised learning

Neurocomputing

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Abstract

This paper uses a simple modification of classic Kohonen network (SOM), which allows parallel processing of input data vectors or partitioning the problem in case of insufficient resources (memory, disc space, etc.) to process all input vectors at once. The algorithm has been implemented to meet a specification of modern multicore graphics processors to achieve massive parallelism. The algorithm pre-selects potential centroids of data clusters and uses them as weight vectors in the final SOM network.In this paper, the algorithm is used on a well-known KDD Cup 1999 intruders dataset.